Rover Lit integrates multi-sensor fusion, AI-powered navigation, and SLAM-based return-to-base with lightweight ML models using Python libraries like TensorFlow Lite and PyKalman. It ensures real-time, efficient exploration with enhanced remote interaction and adaptive decision-making.
Type
Personal Project
Timeframe
3 months
Toolkit
Python
Year
2019
Problem
Designing a cost-effective rover mechanism with real-time object detection, adaptive navigation, and fail-safe mechanisms poses challenges in integrating advanced features on low-resource devices. Existing solutions often lack multi-sensor fusion, efficient edge computing, and autonomous navigation capabilities, limiting their performance and reliability in dynamic environments.
Solution
Rover Lit employs advanced Python libraries like OpenCV, TensorFlow Lite, PyTorch, and PyKalman to integrate multi-sensor fusion and lightweight ML models for real-time object detection and interference management. Using Reinforcement Learning (RL) for autonomous path planning. Enhanced remote telemetry via a live dashboard gives users full control and situational awareness.
To beat the monster, we had to draw the monster. That's why we dive into deep product analysis, detected and prioritized the following issues:
Getting more information about the target audience of business travelers and digital nomads made us suggest the Team change the direction of the product to become a mobile-first platform. A smartphone is a thing every person own so the product will spread faster and the platform engagement is going to be increased dramatically. And that's what we aiming to, right?
